Performance of large language models in the optical diagnosis of colorectal polyps
Multimodal AI models show promise in polyp classification, but clinical reliability remains a key hurdle.
Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potential for image-based diagnosis. We aimed to evaluate the diagnostic accuracy of MLLMs in classifying c...
ArXiv CS.CV
Source
Generated insight by OnAirToday AI
Large language models are beginning to demonstrate significant potential in augmenting gastrointestinal diagnostics by accurately classifying colorectal polyps. While current performance is approaching expert consensus, clinical integration requires further validation to meet stringent medical safety standards.
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.